Personnel inspection with threat detection and discrimination

ABSTRACT

A method includes receiving, from a plurality of magnetic field receivers including magnetic sensors, data characterizing samples obtained by the plurality of magnetic field receivers, the samples of a combination of a first magnetic field and a second magnetic field resulting from interaction of the first magnetic field and an object; determining, using the received data, a polarizability index of the object, the polarizability index characterizing a magnetic polarizability property of the object; classifying, using the determined polarizability index, the object as threat or non-threat; and providing the classification. Related apparatus, systems, techniques, and articles are also described.

RELATED APPLICATIONS

This application is a continuation of U.S. patent application Ser. No.17/550,170, filed Dec. 14, 2021, which is a continuation of U.S. patentapplication Ser. No. 16/664,565 filed Oct. 25, 2019, which claims thebenefit of and priority to U.S. Provisional Patent Application No.62/751,490 filed Oct. 26, 2018, the entire contents of each are herebyexpressly incorporated by reference herein.

TECHNICAL FIELD

The subject matter described herein relates to a personnel inspectionsystem, which in some example implementations, can be capable ofperforming threat detection and discrimination without personal itemdivestment.

BACKGROUND

Airport security attempts to prevent any threats or potentiallydangerous situations from arising or entering the country. Some existingradio frequency (RF) imaging systems (such as those utilized by airportsecurity for passenger screening) are large, expensive, and requireindividuals to remain stationary while an antenna rotates around thestationary individual to capture an image. In addition, these existingRF imaging systems can require divestment of personal items such as cellphones, keys, wallets, and the like, by the individual under inspection.Such divestment requirement can reduce throughput and usability of theimaging systems.

Some existing inspection systems, such as walkthrough metal detectors,can include coils to generate and measure changes in a magnetic fieldcaused by magnetic or conductive materials (e.g., metallic) passingthrough the magnetic field. These existing inspection systems can becapable of measuring for metallic objects passing through a thresholdbut can lack any ability to distinguish personal items such as a cellphone, laptop, keys, belt buckle, and the like from threats, such asfire arms or improvised explosive devices. Accordingly, these exampleexisting inspection systems require divestment of personal items therebylimiting their throughput and usability.

SUMMARY

In an aspect, a method includes receiving, from a plurality of magneticfield receivers including magnetic sensors, data characterizing samplesobtained by the plurality of magnetic field receivers, the samples of acombination of a first magnetic field and a second magnetic fieldresulting from interaction of the first magnetic field and an object;determining, using the received data, a polarizability index of theobject, the polarizability index characterizing a magneticpolarizability property of the object; classifying, using the determinedpolarizability index, the object as threat or non-threat; and providingthe classification.

One or more of the following features can be included in any feasiblecombination. For example, the polarizability index of the object cancharacterize at least a shape, a permeability, and a conductivity of theobject. The polarizability index of the object can include a complextensor including at least six elements characterizing directionalpolarizability components of the object at one or more frequenciesemployed by the transmitting system. Determining the polarizabilityindex can include solving a set of trial solutions via a precomputedpseudo-inverse, determining a residual for each of the trial solutions,and selecting the trial solution resulting in a smallest residual.Determining the polarizability index can include defining a set of trialsolutions, each trial solution including a location, a speed, and atime-shift; calculating an associated polarizability index and anassociated residual for each trial solution; and selecting a final trialsolution, the final trial solution including the trial solution of theset of trial solutions that is associated with the smallest residual.The method can further include localizing the object within a volumeunder inspection, the localization including determining an objectspeed, an object position, and an object time-offset to a predeterminedplane. The method can further include generating one or more signals fordriving a magnetic field transmitter at a frequency that is less than1,000 Hertz (Hz).

The plurality of magnetic field receivers can include flux gate sensorsthat measure a magnitude and phase of the combination of the firstmagnetic field and the second magnetic field in at least three axis atlocations of the plurality of magnetic field receivers. The samplesobtained by the plurality of magnetic field receivers can characterize amagnitude and a phase. Classifying, using the polarizability index, theobject as threat or non-threat can include comparing the magnitude, thephase, and the polarizability index to a library of predetermined threatsignatures. The first magnetic field can be generated by at least afirst magnetic field transmitter and a second magnetic fieldtransmitter. The first magnetic field transmitter and the secondmagnetic field transmitter can be oriented in a first direction. Thefirst magnetic field transmitter and the second magnetic fieldtransmitter can be spatially offset transverse to a predefined directionof motion and transverse to the first direction.

In another aspect, a system can include a magnetic field transmitterconfigured to generate a first magnetic field; a plurality of magneticfield receivers including magnetic sensors, the plurality of magneticfield receivers configured to sample a combination of the first magneticfield and a second magnetic field resulting from interaction of thefirst magnetic field and an object; and at least one data processorconfigured to at least: receive data characterizing the samples obtainedby the plurality of magnetic field receivers; determine, using thereceived data, a polarizability index of the object, the polarizabilityindex characterizing a magnetic polarizability property of the object;classify, using the polarizability index, the object as threat ornon-threat; and provide the classification.

One or more of the following features can be included. For example, atransmit driver can be coupled to the magnetic field transmitter andconfigured to generate one or more signals for driving the magneticfield transmitter at a frequency that is less than 1,000 Hertz (Hz). Thetransmit driver can be configured to generate one or more signals fordriving the magnetic field transmitter at a first frequency and a secondfrequency. The magnetic field transmitter can be driven at the firstfrequency and the second frequency. The first frequency can be between 5Hz and 40 Hz and the second frequency can be between 100 Hz and 1000 Hz.A second magnetic field transmitter can be included. The transmit drivercan include: first drive electronics to drive the magnetic fieldtransmitter; and second drive electronics to drive the second magneticfield transmitter. The samples can be obtained by the plurality ofmagnetic field receivers and can characterize a magnitude and a phase.Classifying, using the polarizability index, the object as threat ornon-threat can include comparing the magnitude, the phase, and thepolarizability index to a library of predetermined threat signatures.

The plurality of magnetic field receivers can include flux gate sensorsthat measure a magnitude and phase of the combination of the firstmagnetic field and the second magnetic field in at least three axis atlocations of the plurality of magnetic field receivers. A dataacquisition base station can be included, coupled to the plurality ofmagnetic field receivers, and configured to filter, demodulate, anddigitize the samples obtained by the plurality of magnetic fieldreceivers. The data acquisition base station can be further configuredto determine in-phase and quadrature data characterizing the secondmagnetic field. Determining the polarizability index can includedetermining a polarizability tensor. Determining the polarizabilityindex can include solving a set of trial solutions via a precomputedpseudo-inverse, determining a residual for each of the trial solutions,and selecting the trial solution resulting in a smallest residual.

The at least one data processor can be further configured to: localizethe object within a volume under inspection, the localization includingdetermining an object speed, an object position, and an objecttime-offset to a predetermined plane. Determining the polarizabilityindex can include: defining a set of trial solutions, each trialsolution including a location, a speed, and a time-shift; calculating anassociated polarizability index and an associated residual for eachtrial solution; and selecting a final trial solution, the final trialsolution including the trial solution of the set of trial solutions thatis associated with the smallest residual. The at least one dataprocessor can be further configured to determine a measure of distortionof the second magnetic field based on an amount of metal present at alocation where the system is deployed.

The system can further include a base plate configured to couple a firstpost and a second post. The magnetic field transmitter can be coupled tothe first post and the plurality of magnetic field receivers can becoupled to the second post. The base plate can include a modularmounting system configured to couple the base plate to a surface uponwhich the base plate is positioned. The modular mounting system caninclude at least one of a plurality of suction cups, a plurality ofgripping mechanisms, a plurality of piercing mechanisms, and a pluralityof auger mechanisms. The base plate can include a base plate framehidden within the base plate. The base plate frame can be configured topermanently install the base plate in a location where the system isdeployed. A proximal end of the first post and a proximal end of thesecond post can be coupled to the base plate. A distal end of the firstpost and a distal end of the second post are not connected to an archwaycoupling the distal end of the first post to the distal end of thesecond post. At least one of an accelerometer or an inclinometer can becoupled to the first post and/or the second post.

The system can further include at least one camera coupled to the firstpost or the second post. The at least one camera can be configured tocapture image and/or video data of the object. The camera can beselected from a group including a depth camera, a rear-facing camera, afish-eye camera, an infrared camera, a thermal camera, a surface mapcamera, an electro-optical camera, and a stereo camera. The system caninclude a first camera coupled to the first post and a second cameracoupled to the second post. The first camera can be configured tocapture image at a first viewing angle and the second camera can beconfigured to capture image data a second viewing angle. The secondviewing angle different than the first viewing angle. The image datacaptured by the first camera and the second camera is registered withina coordinate system of the system. Responsive to classifying the objectas a threat, the processor can be further configured to generate agraphical indication identifying a location of the object and to overlaythe graphical indication atop the image data in a display.

The details of one or more variations of the subject matter describedherein are set forth in the accompanying drawings and the descriptionbelow. Other features and advantages of the subject matter describedherein will be apparent from the description and drawings, and from theclaims.

DESCRIPTION OF DRAWINGS

FIG. 1 is a system block diagram of an example personnel inspectionsystem that can be capable of performing threat detection anddiscrimination without personal item divestment;

FIG. 2 is a diagram illustrating four example plots of transmitterspatial arrangements;

FIG. 3 is a diagram illustrating an arrangement of an example personnelinspection system according to some implementations;

FIG. 4 is a process block diagram illustrating an example process for anexample inspection system according to some aspects of the currentsubject matter;

FIG. 5 is a process block diagram illustrating an example process fordetermining a polarizability index of an object in an example inspectionsystem according to some aspects of the current subject matter;

FIG. 6 is a process block diagram illustrating an example process fordetecting objects on individuals within large groups of persons in anexample inspection system according to some aspects of the currentsubject matter;

FIG. 7 is a diagram illustrating an exemplary implementation of apersonnel inspection system;

FIG. 8 is a diagram illustrating an exemplary configuration of apersonnel inspection system including a plurality of sensors andtransmitters at different locations; and

FIG. 9 is a diagram illustrating an exemplary configuration of apersonnel inspection system including a plurality of cameras.

Like reference symbols in the various drawings indicate like elements.

DETAILED DESCRIPTION

Personnel inspection systems are used to detect threats which can beintroduced to particular area the inspection system seeks to protect. Acommon personnel inspection system can include, for example, a metaldetector configured at an entrance to a courthouse or a stadium, or abody scanner at airport. These inspection systems are configured togenerate data that can be processed to determine an absence of a threat.A threat can be considered any individual, object or element passingthrough the system, which if allowed to enter the protected area cancause damage, introduce security concerns, and/or disrupt events oractivities occurring in the protected area. For example, a firearm is athreat which personnel inspection systems seek to detect at airports orstadiums. Typically, personnel inspection systems are configured todetect threats which include metallic objects.

Traditional personnel inspection systems have a number of drawbacks.Personnel inspections systems are commonly configured to scan orevaluate data associated with a single individual at a time, such as aqueue of individuals at a security area of the airport. Each individualmust be scanned or processed before another individual can be scanned orprocessed for threat detection, which can result in delays and long waittimes to enter the protected area. Individuals commonly experienceelevated levels of anxiety and distrust when being evaluated for thepresence of potential threats in traditional personnel inspectionsystems.

Traditional personnel inspection systems also require individuals toremove all potential clutter objects, such as any metal objects, priorto entering the area at which the inspection system is deployed. In thisway, traditional inspection systems can broadly identify a potentialthreat as any detected metal object that may remain on an individualpassing through the inspection system without discriminating for thesize, type, or composition of the object. Using this type of broad,binary discrimination threshold can result in large rates of falsealarms and require individuals to undergo subsequent inspectionprocessing to clear objects that were inaccurately identified as threatobjects. For example, traditional inspection systems cannot typicallydiscern the object and material properties of a belt buckle uniquelyfrom those of a firearm. In traditional inspection systems, both objectsare equally detected and characterized as potential threats, yet thebelt buckle poses much less of a threat, or even no threat, compared tothe firearm. Individuals typically have a number of metal objects ontheir body which may be falsely identified as threats in traditionalinspection systems. Shoe or boot grommets, belt buckles, glasses, aswell as cell phones, laptops, hearing aids, and pacemakers all includemetal which traditional inspection systems may falsely identify asdetected threats. As a result, personnel inspection systems require allpotential threat objects to be removed or divested from the individualpassing through the inspection system.

The current subject matter can include an improved personnel inspectionsystem, which in some example implementations, can be capable ofperforming threat detection and discrimination in high clutterenvironments in which individuals may be carrying personal items such ascell phones and laptops and without personal item divestment. In someimplementations, a personnel inspection system can perform threatdetection and discrimination with high throughput that allowsindividuals to pass through the detector at normal walking speeds suchthat individuals are not required to slow down for inspection and, insome implementations, the inspection threshold can allow for multipleindividuals to pass through the threshold side-by-side (e.g., two ormore abreast).

Advantages of this improved personnel inspection system can includehigher throughput of individuals being evaluated, reduced incidence offalse alarms due to more accurate discrimination of metal objects asthreats or non-threats, and reduced stress levels and improved emotionalresponse for individuals being evaluated using the improved personnelinspection system. In addition, the improved personnel inspection systemcan more accurately distinguish metal objects present on an individualpassing through the improved inspection system as threats or non-threatswithout requiring the individual to remove the metal object from theirbody. The data that is collected, processed, and generated by theimproved inspection system can also be used within the context of othersecurity-focused operations such as notification to system operators ofindividuals who are in possession of a detected threat object, trainingexercises for inspection system operators or supervisors, as welloverall process improvement of security procedures which may occur priorto or after individuals are screened or evaluated using the improvedinspection system.

Some example implementations of the improved inspection system disclosedherein can include a continuous-wave magnetic detection system of highsensitivity, capable of detecting disturbances in its transmitted fieldof up to one part in 10,000. To facilitate this sensitivity, the systemcan be configured to transmit a stable magnetic field and to measure thetransmitted magnetic field using a low-noise method, as magneticdisturbances caused by unintentional system noise can be very difficultto distinguish from magnetic disturbances caused by metallic objects. Inthis context, system noise can encompass a number of signalinterferences, including traditional electronic noise, amplitudevariations in the transmitted magnetic field, and/or digital error,which can be introduced by harmonic mismatches between intentionalsignals and sampling rates associated with analog to digital conversion.

Some example implementations can include an active magnetic system thatcan acquire a series of magnetic field measurements of an observationaldomain; determine in-phase and quadrature components of the magneticfield measurements; determine a measure of polarizability (e.g., apolarizability tensor, polarizability index) of an object in theobservational domain; localize the object including determining speed,position, and time offset of the object; and perform threat detectionand/or discrimination of the object in the presence of clutter using themagnetic field measurements, the polarizability, and/or the localizationinformation. In some implementations, the system can be configured todetect for firearms and/or improvised explosive devices (IEDs).

In some implementations, the system can determine a polarizability ofobjects under inspection and can perform threat detection anddiscrimination (e.g., classification) using the polarizability of theobjects. By determining and utilizing the polarizability of objects,certain threats, such as fire arms and improvised explosive devices, canbe more accurately detected, resulting in improved personnel inspectionsystems.

In personnel inspection systems configured with magnetic-field sensingto detect illegal or threat objects, such as firearms, the frequencyband that is used to optimize the signal-to-noise (SNR) ratio is not thesame as the frequency band that is used to optimize discriminationbetween threat and non-threat objects. In addition, one or both of theoptimized frequency bands can be too low to be effectively detected bythe receivers of the system. A challenge in personnel inspection systemsconfigured with magnetic-field sensing can include determining how bestto measure for detected threat objects at the appropriate frequency bandwhile retaining the benefit provided by both optimized frequency bands.

The improved inspection system described herein can be configured tointerrogate an object in such a way that it gets information in a firstfrequency band with good SNR properties, as well as a second frequencyband with good discrimination properties, the first frequency band beingdistinct from the second frequency band. The use of fluxgates, either inaddition to or instead of induction-coil receivers, can allow the systemdisclosed herein to measure very low frequency (e.g., sub 1 kHz)magnetic fields. By using frequency band measurements from the firstfrequency band with good SNR properties to determine certain propertiesof the object (such as its location, speed, orientation, or the like),before recovering additional properties of the object via the secondfrequency band with good discrimination properties. Some exampleimplementations of the magnetic sensing algorithm and the systemdescribed herein can exploit the benefits of both frequency bandssimultaneously.

For example, many consumer electronics contain non-ferrous metals likealuminum and copper, while many firearms contain ferrous metals likesteel. Due to the fact that eddy currents scale with frequency, themaximum distinction between ferrous and non-ferrous metals can beachieved by exciting an object with magnetic fields at very lowfrequencies (sub 1 kHz). Such low frequencies can be efficientlymeasured using fluxgate receivers as compared to induction-coils, whichare simpler and easier to build at higher frequencies. However, fluxgatereceivers (and their digitizing electronics) often have noisecharacteristics that get better at higher frequencies. The system andmagnetic sensing algorithm described herein utilize frequencymeasurements at low frequencies (e.g., ˜30 Hz) and at higher frequencies(e.g., ˜230 Hz). For example, the system and magnetic sensing algorithmdescribed herein can utilize frequency measurements between about 5 and100 Hz and between and between about 100 and 1000 Hz to simultaneouslyexploit enhanced discrimination characteristics of low frequencies andthe superior SNR of the high frequencies to improve metal objectdetection and discrimination. Accordingly, in some implementations, thesystem can operate at frequencies below 1 kilo hertz (Hz) in order toimprove performance of detecting and discriminating firearms in thepresence of common personal items such as cell phones. At relativelylower frequencies (under 1 kHz, for example), magnetic contributions tothe magnitude of polarizability can dominate over conductivecontributions to the magnitude of polarizability. As a result, thesignal magnitude may be driven less by the total metallic content of athreat than by the material that is unique to the characteristics ofmany threats and absent from typical consumer electronics.

The above description of the processing performed by the inspectionsystem and magnetic sensing algorithm described herein can be furtherconsidered with regard to a single object passing through the inspectionsystem. The single object will have some properties that vary withfrequency, such as material properties and/or magnetic dipole momentsand/or polarizability tensor elements and will have some properties thatare shared across the frequency bands such as location, orientation, andspeed. The inspection system and magnetic sensing algorithm describedherein provide enhanced detection capabilities by using measurements fordetermining the frequency-invariant properties of the object, and usingthe good discrimination properties primarily for classification.

In some implementations, the magnetic sensing algorithm described hereinis configured to initially perform a retrieval operation at a frequencyband with favorable SNR characteristics to solve for location,orientation, and speed, and subsequently retrieves an object signature,such as material properties, magnetic dipole moments, and/or apolarizability tensor or index of the object at the other frequencyband. Information determined from the first retrieval operation can beused to constrain the second retrieval and improve the overall accuracyof detection.

In other implementations of the magnetic sensing approach describedherein, the magnetic sensing algorithm can retrieve all objectproperties in a single step by using a weighted cost function thatevaluates the higher frequencies more closely for thefrequency-invariant properties. In both implementations, the propertiesof the object at the more discriminating frequency band will berecovered with greater fidelity than if they had been recoveredindependently. Subsequently, these properties can be used in aclassification step that decides if the object belongs to a particularcategory, such as firearm or consumer electronic device.

FIG. 1 is a system block diagram of an example inspection system 100that can be capable of performing threat detection and discriminationwithout personal item divestment.

As shown in FIG. 1 , the system 100 includes magnetic receivers 105coupled to a data acquisition base station 115. The data acquisitionbase station 115 can be configured to filter, demodulate, and digitizethe magnetic field measurement data received from the receivers 105. Thetransmitters 106 and magnetic receivers 105 can be arranged to probe anobservational domain (OD) 107, sometimes referred to as a “scene”, suchas a threshold or other defined region. The OD 107 can be considered toinclude voxels defining a volume. The OD 107 can be a single continuousregion or multiple separate regions. The system 100 also includetransmitters 106 coupled to a transmission driver 160. The transmissiondriver 160 can be configured to generate a signal to drive transmitters106. The system 100 also includes a processing system 120 configured toanalyze the received magnetic field measurements. The processing system120 includes a data acquisition module 130, a calibration module 135, areconstruction module 140, an automatic threat recognition module 145, arendering module 150, and a memory 155. The system 100 can also includea display 165 for providing output; and a sensor 125 to provideadditional inputs to the system 100.

In some implementations, the system can be configured to operate as adistributed lock-in amplifier, utilizing a synchronous homodyne digitaldual-phase demodulation technique to accurately extract in-phase (I) andquadrature (Q) information from the system's specific transmittedfrequencies. Demodulation can be achieved by digitally mixing ormultiplying the desired signal with a reference signal and subsequentlyfiltering the result using a low-pass filter. The reference signal canbe a directly measured signal related to or derived from the drivingsignal used in the transmitter, or can be a synthetic analogue. Byutilizing two versions of the reference signal, one phase shifted ortime-delayed from the other, the amplitude, phase, and/or I and Q of themeasured signal can be reconstructed.

In some implementations, the transmission driver 160 can include acombined set of digitally-controlled high-accuracy direct digitalsynthesis (DDS) waveform generators, a digitally-controlled summingprogrammable-gain amplifier (PGA) circuit, and a closed-loop class-Dpower amplifier with enhanced power supply rejection ratio (PSRR). Sucha system provides flexibility in the frequency and amplitude of thetransmitted waveforms while achieving high stability in the transmittedmagnetic fields required to meet necessary signal-to-noise ratios in themeasured data. The system 100 can digitally control an amplitude and afrequency of the transmitted magnetic fields. Digitally controlling theamplitude and the frequency of the transmitted magnetic field can beperformed in a dynamic manner and in an arbitrary or ad-hoc manner. Insome implementations, the system can include a closed-loopmicrocontroller-based feedback system configured to measure anddynamically adjust the per-frequency amplitude of the transmitted fieldthereby increasing the stability and predictability of the system.

Transmitters 106 can include at least two wire-loop transmitters capableof generating a magnetic field according to a driving signal having anoperating (e.g., characteristic) frequency (e.g., a modulationfrequency). The transmitters 106 can operate at 30 Hz and 130 Hz, forexample. In general, a wire-loop can be considered to reside within aprimary plane. In some implementations, the system 100 can includetransmitters arranged to deliver fields with sufficient diversity toprobe all cardinal directions (e.g., cartesian coordinates) throughoutthe OD 107. In a static system where objects under inspection arestationary, at least three transmitters can be included that are eitheroriented orthogonally (e.g., the primary plane of each of the threetransmitters can be oriented orthogonal to one another), or else offsetin space. If the object is undergoing motion in a particular direction,as in an object passing through the inspection system 100, twotransmitters can be used if they are oriented orthogonal to thedirection of motion or spatially offset transverse to both the directionof motion and their shared orientation. This configuration represents areasonable constraint on object motion (e.g., in one direction) and canfurther represent the fewest number of transmitter coils capable ofachieving sufficient field diversity to fully probe a given object.

As shown in FIG. 1 , the transmitter driver 160 can generate one or moresignals for driving the transmitters 106. In some implementations, thetransmitters 106 can be driven by cycling through the transmitters intime, driving one, then another, until all desired measurements arecaptured. A benefit of such approach can include that the driveelectronics can be shared across all of the transmitters 106. However,this approach can impose a duty-cycle on each transmitter 106, reducingits signal-to-noise ratio. In such a configuration, the transmitters 106may not be measured at the same instant in time, which, if the object isin motion, may introduce motion-induced artifacts.

In some implementations, the transmitters 106 can be drivensimultaneously, but at slightly (e.g., 10 Hertz (Hz)) offsetfrequencies. The frequencies can be offset enough such that they can bedistinctly demodulated in post-processing, which can be set by thebandwidth necessary to resolve the object's motion, which can be about5-10 Hz for objects moving at typical walking speeds of 1.3 meters persecond (m/s). At the same time, the frequencies can be chosen to besimilar enough that dispersion in the polarizability is negligible. Insome implementations, the offset can be 10 Hz, which can be considered anegligible difference at all but vanishing frequencies. In this examplefrequency multiplexing approach, the transmit driver 160 can includeseparate drive electronics to drive each transmitter separately, whichcan enable improved signal-to-noise ratios without (and/or reducing) therisk of motion blur.

In some implementations, the transmission driver 160 can be capable ofgenerating driving signals that can be distributed to transmitters 106,which can establish a fully phase coherent measurement system across allreceive-transmit pairs. In addition, the driving signal can be providedas a reference signal routed from the transmitter driver 160 to the dataacquisition base station 115, which can be utilized for demodulation, asdescribed more fully below.

The magnetic receivers 105 can include flux gate sensors, which candirectly measure the magnetic field (e.g., magnitude and phase) ascompared to wire coils, which measure a rate of change of magneticfield. In some embodiments, one or more of the receivers 105 can include3-axis flux gate magnetometers. In some embodiments, one or more of thereceivers 105 can include 2-axis flux gate magnetometers. Flux gatemagnetometers can be advantageous in that they can operate with highsensitivity, high linearity and a low noise floor as compared to coilreceivers. The receivers 105 can provide accurate magnetic measurementat frequencies too low for traditional methods.

A flux gate sensor can measure the amplitude of a magnetic field inthree axis (e.g., x, y, and z) at the location of the flux gate sensor.A flux gate sensor can include a sense coil surrounding an inner drivecoil that is closely wound around a highly permeable core material, suchas mu-metal. An alternating current can be applied to the drive winding,which can drive the core in a continuous repeating cycle of saturationand unsaturation. In the presence of an external magnetic field, withthe core in a highly permeable state, such a field is locally attractedor gated through the sense winding. This continuous gating of theexternal field in and out of the sense winding induces a signal in thesense winding, whose principal frequency is twice that of the drivefrequency, and whose strength and phase orientation vary directly withthe external field magnitude and polarity.

In some implementations, flux gate sensors can be utilized withoperating frequencies below 1 kHz, such as 130 Hz and 30 Hz. At theserelatively low operating frequencies, flux gate sensors can operate withimproved noise-floors, for example, some flux-gates can achieve avolt-to-field ratio on an order of 20 micro-Volts/nano-Tesla.

Data acquisition base station 115 can demodulate, filter, and digitizedata received from receivers 105. The data acquisition base station 115can aggregate the received data, determine in-phase and quadrature data(I and Q data, respectively) from the received and aggregated digitizeddata, and transmit the aggregated data as in-phase and quadrature datato processing system 120. Filtration and amplification of the rawmagnetometer signals provided to the data acquisition module 130 allowsthe system to achieve high dynamic range in frequencies of interest,e.g., frequencies below 1 kHz, such as 130 Hz and 30 Hz, by rejectinglarge ambient direct current (DC) magnetic signals. The bandwidth anddesign of the filters used in the hardware and/or the software of thesystem 100 can be selected to reject unwanted signals in theenvironment, such as 50 and 60 Hz signals generated by alternatingcurrent (AC) lines, while maintaining sufficient bandwidth in thedemodulated signal to recover the motion of the object.

Sensor 125 can include an infrared (IR) camera, thermal camera,ultrasonic distance sensor, video camera, electro-optical (EO) camera,and/or surface/depth map camera. Sensor 125 creates an additionalinformation image or video, such as an optical image, of at least the OD107. In some implementations, sensor 125 transmits images or video toprocessing system 120 for further analysis. System 100 can includemultiple sensors 125. Sensor 125 can also be used to detect for thepresence of a target in the OD 107. Detecting the presence of a targetin the OD 107 can be used to trigger scanning by the system 100. In someimplementations, sensor 125 can include a radio frequency identification(RFID) reader.

The system can also present an image to an operator via display 165 inwhich the visible portion of the visitor and/or their belongings mostlikely to contain the object(s) is segmented, highlighted, or otherwisemade to provide notice to an operator and aid in the operator'sresponse. In addition, aspects of the object can be determined based onthe images obtained from the depth camera. The obtained aspects can beassociated with classification of the object. For example, if the objectis in plain view, the magnetic sensing algorithm can determine theobject class, such as determining that the object is a laptop or anumbrella. If the object is concealed, the magnetic sensing algorithm candetermine a part of the person's body or a location on the person wherethe object is concealed, such as a pocket of the person's clothing, anankle or wrist of the person, or a bag that the person may be carrying.Data associated with these locations can be combined with informationderived from the magnetic field data in a classification step that usesall available information to achieve greater predictive accuracy duringthreat detection.

Processing system 120 includes a number of modules for processingmagnetic field data and additional information images from sensor 125 ofthe OD 107 including data acquisition module 130, calibration module135, reconstruction module 140, automatic threat recognition module 145,rendering module 150, and a memory 155.

Data acquisition module 130 acquires a time-series of voltagemeasurements which represent magnetic field measurements from the DASbase station 115 and additional information images from the sensor 125.In some implementations, the sampling rate of the data acquisitionmodule 130 is derived from the same master clock used to generate thetransmitted fields via the transmitters 106. For each receiver 105, dataacquisition module 130 derives I and Q data from this time-series inpost-processing via demodulation with an accompanying reference signal.Timing of the I and Q data can be synchronized across receivers 105 anddata acquisition module 130 can publish the synchronized data as frames(e.g., time slices) for further analysis by system 100.

In some implementations, the master clock of the system 100 can bedistributed across multiple meters of space in the system, using aninternal network of low-jitter low-skew clock fanouts and low voltagedifferential signaling (LVDS) converters. This configuration can enablea sampling rate to be an integer harmonic of every transmittedfrequency, eliminating digitization errors which otherwise damage thesensitivity of the system. By configuring each device in the dataacquisition process 130 on the same clock domain, receivers 105, whichcan be located meters apart, can be correctly assumed to be receivingsamples at the same time intervals, with no drift due to frequencymismatching. Thus, for a given frame, data acquisition module 130publishes a set of data for each receiver 105 and sensor 125. In someimplementations, data can be acquired and frames can be published at arate sufficient to resolve the carrier frequencies.

In some implementations, data acquisition module 130 removes the staticbackground signal (e.g., the primary field). In some implementations,the data acquisition base station 115 can remove the static backgroundsignal (e.g., the primary field) such that the I and Q datacharacterizes the secondary field and not the primary field.

Calibration module 135 applies calibration correction to the publisheddata. Calibration corrections can include compensating the publisheddata for serial time-sampling. In addition, calibration module 135 cancompare measured primary fields to one or more field model predictions,and compensate for any differences. In some implementations, calibrationcan account for amplitude and phase changes of the transmitters thatoccur due to normal wear and tear, manufacturing variations, ortemperature changes.

Reconstruction module 140 transforms the calibrated data into imagesand/or feature maps. An image can be created for each receiver 105,and/or based on a composite of measurements obtained by multiplereceivers 105. The reconstruction module 140 can include determining thepolarizability measure (e.g., tensor) and localization of an object.

Polarizability can be characterized as a proportionality constantrelating an object's far-field response to a primary field that inducedit. It can have units of volume, and can depend on the shape,permeability, and conductivity of the object, as well as the frequencyof the applied field. In order to determine the polarizability, in someimplementations, a best-fit algorithm can be utilized to implement aminimum residual matched filter.

The transmitter fields can be calculated from models of rectangularcoils. The receiver fields can be calculated from dipole fields alongthe particular axis of the sensor, such that a 3-axis receiver node istreated like 3 independent and orthogonal dipoles.

In some implementations, image data from the sensor 125 can be used tofurther enforce the sparsity constraint beyond that supplied by a-prioriknowledge of items or subjects that may occupy the OD 107. Specifically,an image of the OD 107 acquired by sensor 125 can be used to determine aspatial location of the target (e.g., which voxels of the OD 107 thetarget resides in and which voxels of the OD 107 are empty). Emptyvoxels contain no objects and therefore can be considered zero forcompressed sensing (e.g., enabling better and/or quicker estimations ofthe solution to the underdetermined linear system).

In addition, an appropriate sized OD 107 can result in a scene that issufficiently sparse for compressed sensing. For example, if an OD 107 isa volume that is 2 meters by 1 meter by 0.5 meters, and is divided into8,000,000 voxels of 5 mm, a typical human located within this OD 107would occupy only about 10% of the voxels at any moment (e.g.,approximately 800,000 voxels). A retrieved set of polarizable objectsfrom a sensor 125 can be used to determine three-dimensional surfaceswithin the OD 107 volume and consequently which voxels the individualresides in. The empty voxels can be forced to zeros when retrieving theset of polarizable objects while non-zeroed voxels can be altered duringreconstruction (e.g., can be considered variables to find an optimalreconstructed solution to the underdetermined linear system).

Reconstruction module 140 can reconstruct one or more magnetic retrievedset of polarizable objects. In addition, reconstruction module 140 cancreate aggregate retrieved set of polarizable objects by combiningmultiple independent retrieved sets of polarizable objects. In someimplementations, reconstruction module 140 can treat all receivers 105as one large sparse aperture and reconstruct a single retrieved set ofpolarizable objects using the information acquired from all receivers105 in the single aperture.

Reconstruction module 140 can perform localization of the object usingmultiple time-slices. Such an approach can use a single model-fittingapproach that solves for the objects location (e.g., x, y, andt-crossing), speed, and polarizability tensor. An example localizationapproach is described more fully below.

Reconstruction module 140 can generate feature maps from thereconstructed images. Feature maps can include characterizations orfeatures of the magnetic measurements. Statistical analysis can beperformed across multiple images. Some example features include fieldmagnitude, field phase, and polarizability tensor properties (discussedfurther below). Other features are possible.

Automatic threat recognition module 145 analyzes the images and/orfeature maps for presence of threat objects. Threat objects can includedangerous items that an individual may conceal on their person, forexample, firearms and explosives. Automatic threat recognition module145 may identify threats using, for example, a classifier that assessesthe feature maps generated by reconstruction module 140. The classifiermay train on known threat features. In some implementations, the threatrecognition process can compare the determined images to a library ofpredetermined polarizability signatures.

In some implementations, features (e.g., classification variables) caninclude field magnitude, phase, and polarizability tensor properties atone or more operating frequencies.

Rendering module 150 generates or renders an image characterizing theoutcome of the threat recognition analysis performed by the threatrecognition module 145. The image can be rendered on display 165. Forexample, rendering module 150 can illustrate an avatar of a scannedperson and any identified threats. Rendering module 150 can illustrate acharacterization that automatic threat recognition module 145 did notdetect any threats.

FIG. 2 is a diagram illustrating four example plots of transmitterspatial arrangements. In plot 205, three transmitters are arranged suchthat they are oriented orthogonally to each other and thus theconfiguration of 205 is capable of measuring all dimensions of a static(e.g., stationary) object. In plot 210, four transmitters are arrangedsuch that they are oriented in the same plane but are offset in spaceand thus capable of measuring all dimensions of a static object. In plot215, two transmitters are arranged orthogonal to one another and thuscapable of measuring all dimensions of an object in motion. Similarly,in plot 220, two transmitters are arranged such that they are orientedin the same plane but are offset in space and thus capable of measuringall dimensions of an object in motion. As discussed in more detailbelow, in some implementations, system 100 can include transmitters 106arranged according to the configuration illustrated in plot 220. Such aconfiguration can provide, in some implementations, a desirable formfactor and reduced cost.

FIG. 3 is a diagram illustrating an arrangement of an example personnelinspection system 300 according to some implementations. Twotransmitters 106 are arranged such that they are oriented in the sameplane but are offset in space and thus capable of measuring alldimensions of an object in motion. In the example, the transmitters 106are coils capable of transmitting between 20 and 1,000 Hz, and can beconfigured to transmit multiple signals offset in frequency so as tooperate simultaneously. For example, the transmitters 106 can operate at30 Hz and 130 Hz. Other offset frequencies are possible, for example,5-10 Hz. In some implementations, the transmitters 106 can operate at(e.g., be driven at) a first frequency between 20 Hz and 40 Hz and at asecond frequency between 120 Hz and 140 Hz. Considerations for selectingfrequencies can include that lower frequencies increase the relativemagnitude of ferrous compared to non-ferrous metals, higher frequenciescan have better noise floors for certain fluxgate sensors, andparticular frequencies (e.g., 50 and 60 Hz, harmonics) can be avoidedentirely due to interference in typical environments. Receivers 105 arearranged vertically on posts (e.g., vertical poles) on either side ofthe transmitter to provide for dual lanes to allow individuals underinspection to pass through the system.

In order to recover a magnetic signature of a detected objectindependent of the location and orientation of the object, it can bedesirable to collect measurements of the object while it is beingexposed to magnetic fields which are transmitted into the object fromorthogonal directions. By arranging the configuration of thetransmitters 106 shown in FIG. 3 in an orthogonal manner, the totalnumber of coils and overall complexity of the inspection system can bereduced, which can reduce overall operating and maintenance costs aswell as the size of the physical footprint of the inspections system.

The configuration of transmitters 106 shown in FIG. 3 , can enable someexample systems disclosed herein to collect measurements of an objectwhile it is interrogated by magnetic fields in largely orthogonaldirections so that a magnetic signature can be determined that islargely independent of location and orientation of the object. At thesame time, the total number of coils and complexity of the system can beminimized for the sake of cost and physical footprint. Field diversitycan be achieved in the system with a minimal configuration oftransmitters, for example two transmitters, by exploiting the motion ofthe object past the transmitters. In addition, the system complexity canbe further reduced by powering the two transmitters simultaneously viaorthogonal time-varying patterns, such as two sinusoids with slightlyoffset frequencies. The frequencies can be similar enough thatdispersion in the polarizability index is negligible.

FIG. 4 is a process block diagram illustrating an example process 400for an example inspection system according to some aspects of thecurrent subject matter.

At 405, the processing system 120 can receive data characterizingsamples obtained by a plurality of magnetic field receivers 105. Forexample, the magnetic field samples can be acquired by receivers 105 andimages (e.g., video) can be acquired by a camera or other sensor 125. Anevent (e.g., identifying that a person is approaching and/or entered theobservational domain) can be identified. For example, an event can beidentified based on event data signal received from a photocell testingoccupancy of the system or motion in a field of view of the camerafeeds. The received event data can cause the system to initiate objectdetection, or alternatively the system can be configured to search foran object in the absence of event data.

At 410, the data acquisition module 130 aggregates an amount of magneticfield data for processing by subsequent components. For example, thedata acquisition module 130 can transmit a copy of a circular buffercontaining magnetic field time samples from a plurality of receivers inthe system. The calibration module 135 can be configured to account fordeviations in the magnetic field data, as compared to a pre-preparedmodel including amplitude, phase, or environmental characteristics. Thereconstruction module 140 can be configured to determine a best-fitobject or objects which can be defined by a set of attributes includingposition, speed, time-offset, and polarizability index. Best fit can bedetermined by a cost function that measures the difference between theactual measurements and those predicted by a model (e.g., residual), andcan also include other attributes that may suggest the plausibility of asolution such as the isotropy of the polarizability index. Thepolarizability index can include a complex tensor including 1-6 uniqueelements characterizing directional polarizability components of theobject at one or more frequencies transmitted by the transmitters 106.These attributes, including the polarizability index, can be determinedsimultaneously or in series using an optimization routine, such as agradient descent algorithm or a nested parameter search. The model canbe, for example, an isotropic or an anisotropic model, with uniform ornon-uniform motion through the scan zone. In some embodiments, featuresderived from the polarizability index of the object can characterize ashape, a permeability, and a conductivity of the object within a unit ofvolume.

At 420, the automatic threat recognition module 145 can classify theobject as a threat or non-threat. A threat/non-threat decision can bebased on physical attributes, such as matching the polarizability indexor a subset of its components against known polarizability indexexamples from previously determined threat objects, such as a gun barrelor a knife. Alternatively, a threat/non-threat decision can use aclassifier trained by in a machine learning process, in which manylabeled examples of threats and non-threats are used to train themagnetic sensing algorithm to determine if a newly detected objectshould be characterized as a threat or non-threat. The classificationcan include making threat/non-threat determinations using the frequencyand the shape information.

Finding multiple objects in the same domain (e.g., either spatially ortemporally) can be indicative of a detection event associated with thevisitor passing through the inspection system disclosed herein. Themagnetic sensing algorithm can combine these objects for classificationby an aggregate classifier that is capable of making additional use ofthe combined information, as compared to classifiers that run on eachobject independently. For example, the classifier can add someproperties of the neighboring objects together, as if they constitute anunderlying large and/or distributed object that is best considered as awhole.

At 425, the classification can be provided. The classification can beprovided, for example, via a display such as display 165. In someimplementations, the classification can be provided to a backendsecurity management system that can coordinate multiple assets (e.g.,screening or inspection devices). In some implementations, theclassification can be stored in memory 155 of the processing system 120.In some implementations, the classification can be stored in a databaseconfigured within the inspection system to store the polarizabilityindex associated with determined threats and determined non-threats.

At 430, the system 100 can repeat the steps of process 400 in aniterative manner.

FIG. 5 is a process block diagram illustrating an example process 500for determining a polarizability index of an object in an example system100 according to some aspects of the current subject matter. In someimplementations, a set of magnetic field samples can be provided as aninput to the process 500. The set of magnetic field samples can becollected simultaneously while probing an object with a set oftransmitted fields. The process 500 can include introducing a set oftrial solutions that can be solved independently via pseudo-inverse,from which the trial solution with the smallest residual can beselected. The process 500 can be performed to determine isotropic andanisotropic polarizability indexes. The process 500 can be extended toinclude samples collected over time while an object experiences motionrelative to the system 100.

At step 505, the automatic threat recognition module 145 can define aset of trial solutions. A series of trial solutions can be indexed toinclude magnetic field samples associated with the possible locations inwhich the object may be present. For each location, the automatic threatrecognition module 145 can determine a corresponding polarizabilityindex based on computing a transfer matrix and a pseudo-inverse for eachof the magnetic field samples to determine the set of trial solutions.In some implementations, the transfer matrixes and the pseudo-inversescan be pre-determined and stored in memory 155.

At step 510, the automatic threat recognition module 145 can calculatean associated polarizability index and an associated residual for eachtrial solution. For example, isotropic polarizability indexes can bedetermined based on selecting the trial solution which best fits themagnetic field samples. When determining an anisotropic polarizabilityindex, the polarizability index can be considered as a complex symmetrictensor defined by 6 unique polarizability elements. The pseudo-inversecan be applied and the polarizability index associated with each of the6 unique polarizability elements can be computed.

At step 515, the automatic threat recognition module 145 can select afinal trial solution. The final trial solution can be selected as thetrial solution with the minimum or lowest residual. During operation,the automatic threat recognition module 145 can capture the magneticfield samples in real-time. The set of pseudo-inverses can be appliedvia matrix multiplication. Subsequently, the transfer matrixes can beapplied to compute the residuals. The trial solution with the lowest orminimum residual can then be selected.

At 520, the automatic threat recognition module 145 can repeat the stepsof process 500 in an iterative manner.

FIG. 6 is a process block diagram illustrating an example process 600for detecting objects on individuals within large groups of personsusing an example inspection system according to some aspects of thecurrent subject matter. The inspection system described herein can alsoenable accurate detection of metal objects on persons walking togetherin groups and can be configured to detect metal objects on large numbersof persons at a time as compared to traditional systems which arelimited to scanning one person passing through the inspection system ata time. Because large groups of people assembled in an unorganizedmanner can make it difficult for an inspection system to determine whento start and/or stop a scan, the magnetic sensing algorithm used in thesystem described herein can dynamically adapt to conditions where manyobjects may be sensed simultaneously and the objects travel through thesystem in an overlapping fashion in time which can result in conditionswhere there is not a clear scan start or stop.

At step 605, the processing system 120 can process sensor data receivedfrom sensor 125 in a streaming manner via the magnetic sensingalgorithm, such that the magnetic sensing algorithm can find one or moreobjects whenever the object(s) happened to pass through the system andrecognizes when insufficient data has been collected in regard to anobject(s). The processing system 120 can determine that a sufficientamount of data has been collected based on the goodness of fit of themodel to the real data, or by the proximity of the found object to theend of the data buffer (e.g., present time). If the processing system120 determines that insufficient data is found, nothing is done, and themagnetic sensing algorithm simply executes again at the next availablemoment or after an allotted amount of additional data has beencollected.

At step 610, the processing system 120 can then store and account forfound objects to avoid detecting the same object over and over again.For example, the modeled signals associated with the found object(s) canbe recomputed and can be subtracted from the measured data beforesearching for the next object. The magnetic sensing algorithm can alsobe biased to search for objects which are located away from thepreviously found objects, for example, by modifying the cost function inthe reconstruction module 140 to penalize objects found in closevicinity with already stored objects.

The processing system 120 and the magnetic sensing algorithm can beconfigured to execute at regular intervals, such that an amount ofelapsed time from when sufficient data has been collected on an objectand when the magnetic sensing algorithm is executed to find that objectcan be minimized. The magnetic sensing algorithm can detect the objectto be found within a time period that extends slightly into the future,e.g., beyond the time point at which the object data was collected andprocessed so far (if this best fits the available data), and uses thisscenario as criteria for knowing when to wait for more data.

At steps 615, the magnetic sensing algorithm of the processing system120 can re-estimate background noise in subsequent executions byopportunistically processing a retained buffer to determine “quiettimes” which can be defined as time-periods where the processed signalsvary little relative to neighboring time-periods. The “quiet time” datacan be used as a proxy for a condition in which no object is presentnear the sensors. A different optimal set of “quiet time” samples can befound for every sensor, accounting for the conditions when an object(s)may be near some sensors but not near other sensors at different pointsin time. When an object is found, it can be stored in a buffer or amemory 155 of processing system 120. This buffer data can be processedin the next iterative execution of the magnetic sensing algorithm, whichsubtracts all previously found objects from the available data so as toavoid contamination/distortion in the present object search.

FIG. 7 is a diagram illustrating an exemplary implementation of apersonnel inspection system 700 according to the subject matterdisclosed herein. The system 700 includes similar components performingsimilar functionality as the components described in relation to thediscussion of FIGS. 1-3 .

Traditional personnel inspection systems commonly include an archwaythrough which an individual must pass for threat evaluation anddetection. In these traditional inspection systems the archway or someother overhead member can carry wiring conveying power and data signalsbetween one or more components of the inspection system, ensurealignment of sensors configured on or within components of theinspection system, and to add stability to the overall structure of theinspection system with respect to environmental conditions such as wind,or uneven mounting surfaces. However, traditional inspection systemsthat include archways or other overhead elements are often aestheticallyunappealing and can create a sense of distrust, anxiety, andclaustrophobia for individuals passing through the archway.

The improved inspection system described in relation to FIG. 7 providesthe advantages of conveying power and data signals to various inspectionsystem components, ensuring alignment of the inspection systemcomponents and reducing emotional response for individuals withoutrequiring them to pass through an archway or overhead element of aninspection system.

A further consideration that the improved personnel inspection systemdisclosed herein addresses is the non-uniformity of the operatingenvironments in which the system may be located. Operating environmentscan include hard, yet somewhat smooth surfaces, such as a tile floor, anasphalt surface, or a concrete floor, as well as softer surfaces whichcan be non-uniform, such as a sand covered surface at a beach entranceor the surface of a grassy field at an entrance to an outdoor musicfestival.

The system 700 disclosed herein can be deployed in a variety ofdifferent locations, indoor and outdoor, and can be configurable basedon different kinds of surfaces on which the system may be positioned.For example, the base plate 710 can be configured as a universal baseplate to which a variety of modular mounting systems can be attacheddepending on the venue at which the system is located and/or the surfaceupon which the system 700 is located. The modular mounting systems canenable positioning, leveling, and coupling or adherence to the surfaceof the location at which the system is deployed for operation. Forexample, the base plate 710 can be configured with suction cups on thebottom side (the side facing the surface of the location at which thesystem is deployed) to secure the base plate 710 to hard surfaces, suchas tile or marble. In some implementations, the base plate 710 caninclude gripping or piercing mechanisms capable of securing the baseplate 710 to soft surfaces, such as carpet. In some implementations, thebase plate 710 can include screw or auger-like mechanisms to couple thebase plate 710 to dirt or terrestrial surfaces. In some implantations,the base plate 710 can be configured with a base plate frame that ishidden within the base plate 710 and provide for permanent installationof the system.

Conventional security systems require visitors to pass through a portalfor detection where an archway is used to carry data signals and powerto/from each part of the system, to ensure proper alignment of thevarious sensors, and to add stability to the overall structure. Improperalignment can generate unwanted biases in the system's performance, andmotion of the archway structure, for example in environments which mayinclude high wind. Improper alignments can cause unwanted distortions tobe generated which are difficult to separate from the desired signals.Archways, however, are visually unaesthetic and can create a sense ofdistrust and unpleasantness for visitors passing through them.

As shown in FIG. 7 , the system 700 disclosed herein can be configuredto provide proper location of the posts 705 which can comprise thereceivers 105 described in relation to FIGS. 1 and 3 , and to provideproper orientation/leveling of the posts 705. In some implementations, abase plate 710 can be configured to receive the posts 705 and ensureproper alignment of the posts at appropriate and predeterminedlocations. In some implementations, the system 700 shown in FIG. 7 caninclude inclinometers configured to instruct an operator in a levelingprocedure. In some implementations, the inclinometers can be used by themagnetic sensing algorithm to compensate for a known misalignment. Insome implementations, the system 700 can include accelerometers tocompensate for structural instability in the system and to track motionin of system components. The accelerometer data can be provided to themagnetic sensing algorithm. In some implementations, signals generatedby the inclinometers and/or accelerometers as well as power to theinclinometers and/or accelerometers the can be routed through the baseplate 710. In some implementations, the signals generated by theinclinometers and/or accelerometers can be transmitted wirelessly to thesystem 100. In some implementations, the base plate 710 can include aplurality of slots to receive the posts 705 or other structures suitablefor mounting one or more sensors 125.

The base plate 710 can be a semi-rigid low profile mat configured toaccurately position the location and orientation of the bases of the Rxand Tx posts 705. An inclinometer 415 can determine relative tilt anglesbetween the Rx and Tx posts 705 at installation. When the system 700determines the relative tilt angles are above some threshold value, theoperator can be alerted and asked to improve or remedy the leveling ofthe posts 705.

For example, in some implementations, the tilt angles can be used torevise the location and orientation of the sensors. The location andorientation of the sensors can be further used to create the sensormodel relied utilized by the reconstruction module 140

In some implementations, accelerometers 720 can be configured to trackthe tilt angles dynamically, which enables the system to track motion ofvarious sensors in the system. For example, when combined with knownfield gradients which can be determined with respect to motion for thevarious sensors, the calibration module 135 can predict distortions tobe expected for measured motions. This prediction can then be removedfrom the measured signal to recover a more accurate representation ofthe signal measured as if there was no motion. In this example, thefields to be modeled and removed can be the measured motion, such as thedisplacement or the tilt of the sensors multiplied by the fieldgradients for each direction/type of motion.

Magnetic-field-based personnel inspection systems can detect or receivemagnetic fields which have been transmitted by the system, such as bytransceivers 106, and an objects secondary fields which can impacted bythe presence of metal in the environment in which the inspection systemis deployed. Environmental magnetic fields, such as those which may bereflected by metal which is nearby the inspection system, such as in ametal floor on which the inspection system located, can negativelyimpact the inspection systems performance to accurately discriminate anddetect threat objects. Determining and characterizing the amount ofmetal in every environment in which the inspection system is located canbe difficult, expensive, and often impossible.

As further shown in FIG. 7 , the system 700 can be configured to accountfor physical, metallic structures which may be present in locationswhere the system is deployed, such as a metal floor. Receivers 105 candetect the transmitted fields 730 generated by the transmitters 106, aswell as secondary fields of an object being detected 735. The secondaryfields can be impacted by the presence of metal in the environment whichmay be present in proximity of system, such as in the floor 725underneath the system. The presence of metal in the floor can negativelyimpact the system's detection performance and accuracy. Locating andcharacterizing metal in each environment can be difficult and expensive.To account for the presence of metal in the environment, the system 700can fit an appropriately parameterized model of metal present in theenvironment based on determining how the magnetic fields 735 transmittedfrom the metal floor 725 are distorted relative to a known model. Thesystem 700 can include an image model, whereby the metal in the floor725 is accounted for as a complex-weighted mirror image of the entiresystem including the transmitters 106 and the receivers 105. Forexample, the image model can model a perfect electric-conductor, whichis known to modify magnetic fields in a very predictable andanalytically describable way. The image model can then be parameterizedby a depth with respect to the sensor coordinate system, and an overallcomplex weight of magnitude less than or equal to 1. Measurements oftransmitted field 730 can be used to perform an optimization routine byfitting the complex weight of the mirror image and the depth of plane ofthe mirror image. Such an optimization routine can search (for example,by trying various pre-defined solutions or via gradient descent) for themodel parameters that best fit the measured data. For example, theoptimization routine can perform the search by trying previously definedsolutions or using a gradient descent optimization. These modelparameters can subsequently be used in the operation of the systemduring threat/non-threat detection. As a result, the system 700 canautomatically detect objects more accurately in deployed environmentswhich may or may not include metal in proximity to the system.

FIG. 8 is a diagram illustrating an exemplary configuration of apersonnel inspection system 800 as disclosed herein including a sensorand transmitters 106 at different locations according to someimplementations. In the illustrated example, the system 800 includes twotransmitters 106, two vertical posts, 805A and 805B, each including aplurality of three-axis fluxgate receivers 810. Each of the three-axisfluxgate receivers are illustrated as an “x” and configured on or withinposts 805A and 805B. The system 800 further includes a verticallyoriented post 805C located adjacent to the two transmitters 106. Thepost 805C includes a plurality of two-axis fluxgate receivers 815. Thenumber of each type of component, such as the transmitters 106 and/orthe fluxgate receivers 105, can be determined by balancing the cost andcomplexity of additional components with the marginal gain that thatadditional component add to the fidelity of the magnetic sensingalgorithm.

The system 800 can further include a camera 820 integrated with thesystem 800. In some implementations the camera 820 can be configured onor within one or more of the posts 805A, 805B, and 805C, as well asconfigured on or within the transmitters 106. The camera 820 cangenerate images and video, in a streamed or recorded manner. Theintegrated camera 820 can be configured with an appropriate viewingangle. For example, in some implementations, the camera 820 can be arear-facing camera.

When an alarm is generated in response to the personnel inspectionsystem detecting a threat, the alarm must be resolved by an inspectionsystem operator or other security team member. Resolving the alarmrequires the operator or security team member to interact with theindividual and to follow security protocols to search the individual.Security protocols may not always be followed properly by the operatoror security team member. A manager of the security team or inspectionsystem operator may be unable to ascertain whether or not appropriatesecurity and searching protocols are being followed. The improvedpersonnel inspection system described in relation to FIG. 8 addressesthese issues.

For example, the camera 820 can provide images or video with asufficient field of view so that supervisors of inspection systemoperators could evaluate an inspection system operator's response to analarm or detected object. In this way, the camera 820 integrated withinthe system 800 can enable a supervisor or manager of inspection systemoperators to assess an operators adherence to screening procedures orpolicies, provide training feedback, provide images or video forevidentiary purposes, record alarm resolution actions taken by anoperator. In some implementations, the image or video from the camera820 can be combined with an image of a detected object generated by thesystem and used to refute or support allegations of improper treatmentby inspection system operators.

A personnel inspection system employing magnetic sensing is limited tosensing metal on an individual passing through the inspection system andis unable to detect the body or physical characteristics of anindividual passing through the inspection system. In this way, thepersonnel inspection system lacks the concept of a person passingthrough the inspection system and can thus generate limited informationabout the individual to an inspection system operator.

The improved inspection system described herein and shown in FIG. 8 caninclude a camera 820 to detect persons passing through the inspectionsystem 800. In some implementations, the camera 820 can be a depthcamera. A depth camera can include a camera configured to use stereovision to calculate depth in images acquired by the depth camera. Depthcameras can include depth sensors, and infrared projectors. In someimplementations, the depth camera can include a color sensor configuredto detect light in the red, green, blue (RGB) scale, also known as RGBsensors. The outputs of the depth cameras can be used to determine alocation, orientation, or disposition of a detected object, as well asthe speed or gait of the object passing through the inspection system.The outputs of the depth cameras can allow the inspection system tocount a number of objects or individuals passing through the inspectionsystem and to track one or more individuals passing through theinspection system. The inspection system disclosed herein, whenconfigured to include a depth camera, can provide a number of advantagescompared to inspection systems which may not include a depth camera.Such advantages can include more rapid identification of threat objectsor individuals and more robust notification or alarm data provided toidentify a threat object or individual.

In such implementations, the depth camera 820 can register itscoordinate system with the automatic threat recognition sub-system 145configured within the inspection system 100 as shown in FIG. 1 .

In this way, the system can have simultaneous knowledge of a visitor'slocation/disposition and any metal objects on them in a commoncoordinate system. Such implementations enable a magnetic sensingalgorithm to be directed towards those voxels which are occupied by thevisitor and/or specific areas on the visitor (such as pockets, bags, andankles). For example, the voxels of the OD 107 can be compared to thepixels obtained via the depth camera to determine which voxels areoccupied (e.g., there is a pixel with coordinates sufficiently close tothis voxel), unoccupied (e.g., there is no pixel with coordinatessufficiently close to this voxel and no chance of occlusion), or unknown(e.g., a pixel has been found that may obscure the OD 107 whether or notan object resides in this voxel). Then, the magnetic sensing algorithmcan be restricted to only search for objects in the occupied and/orunknown voxels.

In addition, using knowledge the speed and gait of the visitor in themagnetic sensing algorithm can improve its accuracy, as well as countand track visitors into and out of the system for statistical reporting.Accuracy of an optimization routine is usually improved when the numberof variables it must solve for can be reduced or constrained. Knowledgeof the speed and location of a visitor would allow constraining orimposition of these attributes on the object that the optimizationroutine is solving for.

In such implementations, the depth camera 820 (or any sensor 125 orcombination of sensors capable of providing both RGB and depth values atvarious pixels, such as a structured light camera or stereo cameras) canbe integrated directly into the system 100 of FIG. 1 . In this way, theimage data obtained by the depth camera can be available to the magneticsensing algorithm. Given knowledge of the location, orientation, andlens properties of the depth camera, the pixels obtained via the depthcamera can be registered to the coordinate system of the automaticthreat recognition sub-system 145 of FIG. 1 , such that each pixel,given a returned depth value, can be translated into the 3D coordinatesystem of the magnetic sub-system by a series of transformations.

As such, the depth camera(s) 820 can identify which voxels in themagnetic sub-system's scan zone are occupied and/or unoccupied at agiven moment in time, and the magnetic sensing algorithm can be directedaccordingly. The speed and gait of the subject can also be estimated andused in the magnetic sensing algorithm to more accurately identify anyconcealed objects. An object which may be discovered via the magneticsensing algorithm using particular 3D coordinate(s). The object can thenbe associated with a subset of pixels in the depth camera image(s) basedon a simple distance threshold.

Furthermore, this subset of pixels can be associated with a largercontiguous object identified and segmented from the depth camera 820,either by its depth values or by its RGB values, or both. For example,the system can identify the outline of a person most likely to becarrying the found object. Properties of, either, the neighborhoodaround the object or the person identified to be holding the object canbe used in its classification. For example, the person's face can becompared to a watch list via a facial recognition algorithm, and pasthistory or knowledge of this person can be used in classifying theobject. At the same time, a threat overlay image made by applying athreat overlay atop of the RGB image can be enhanced to aid operatorrecognition by highlighting either the visible container of the objector the person holding the object, or both. This can help improve anoperator's reaction time and accuracy in resolving an alarm triggered bythe system.

FIG. 9 is a diagram illustrating an exemplary configuration of apersonnel inspection system 900 as disclosed herein including aplurality of cameras according to some implementations. By configuringthe inspection system 900 with a plurality of cameras, the inspectionsystem can better determine which particular individual and where on theparticular individual to search. Personnel inspection system which lackthreat localization using multiple cameras to generate multiple viewingangles allow large, unorganized crowds of individuals to be screenedwithout forming queues for individual screening and can identifypotential threats localized in three dimensions. Additionally, when analarm associated with a detected threat is generated, the use ofmultiple cameras generating multiple view angles enables the system tovisually identify an individual associated with the detected threatusing the captured images to facilitate informing an inspection systemoperator or other security personnel where to search the identifiedindividual for the detected threat. Alternate solutions, which may uselights or a solely still image to identify an individual associated witha detected threat, may only provide an occluded viewing angle from asingle camera. The single view angle may be insufficient to safely andefficiently identify a detected threat on an individual among a crowd ofindividuals passing through the inspection system.

As shown in FIG. 9 , the system 900 includes posts 905A and 905B. Theposts 905A and 905B include a plurality of three-axis fluxgate receivers910. Post 905C includes a plurality of two-axis fluxgate receivers 915.Post 905C also includes transmitters 106.

In some implementations, multiple individuals can pass between the posts905A, 905B, and 905C at any one time. For example, two people may passthrough posts 905A and 905B at the same time which can result inobscuring the field of view of a single sensor, such as sensor 125described in relation to FIG. 1 . As a result, in implementationsincluding a single sensor, the field of view of the sensor can becomeoccluded. In some implementations, multiple sensors can be included inthe inspection system to avoid an occluding field of view at a singlesensor and to provide multiple viewpoints.

As shown in FIG. 9 , multiple cameras, such as cameras 905A and 905B,can be configured within the personnel inspection system 900. Thecameras 905A and 905B can be registered to the system's coordinatesystem representing the coordinates of the OD 107 to perform threatlocalization from multiple viewpoints. As objects are tracked throughtime, each camera can determine the location of the threat across orwithin multiple time-slices. Thus, even if a viewing angle from a singlecamera 905A is occluded at one moment in time, the likelihood that theviewing angle of a second camera 905B is also occluded at all availablemoments in time is very low. The system can provide the inspectionsystem operator images and/or videos from all viewing angles and canutilize the images and/or videos from one or more cameras to determinethe actual threat location. In some implementations, the renderingmodule 150 can be further configured to automatically select optimalviewing angles and/or time-slices for presentation to aid the operator'srecognition and response to a detected threat. For example, the imagethat has the greatest number of pixels occupied in the vicinity of thefound object is likely to give a reasonable and un-occluded view of theobject.

For example, as shown in FIG. 9 , the system 900 includes two fisheyecameras, 905A and 905B. Each camera is mounted on opposing posts 905Aand 905C in order to capture a scan zone from two very distinct viewingangles. Each pixel in the images and/or videos generated by both camerasis mapped to the coordinate system of system 900 at multiple planesalong the direction of an object or person passing through a lane formedbetween posts 905A and 905C. The system 900 can be configured togenerate an alarm localized in a particular plane of view associatedwith camera 905A or 905B. Visual representations or threat indicatorscan be overlaid on images and/or video generated by each camera andcentered on the appropriately registered pixels corresponding to thedetected threat. The system 900 can be configured to repeat theseoperations for multiple planes of view as the object or person passesthrough the system so as to produce a threat overlay image or video fromeach camera.

The videos and/or images from one or all cameras can be shown to theoperator via a computing device communicatively coupled to the system900, such as a laptop, tablet or other mobile computing deviceconfigured with a display. In some implementations, the system canutilize a threshold criterion for determining if a viewing angle of oneof the cameras is occluded. The system 900 can determine occlusion, forexample, by determining a depth estimate of the pixels in question byevaluating motion in the video stream, or by incorporation of a depthcamera. If the system 900 determines that the depth estimate is notconsistent with the 3D coordinate returned by the system , then theviewing angle should be considered occluded, and the system candetermine that the image will not be useful in guiding the response ofan operator.

In some implementations, the inspection system 900 can process theadditional sensor data, such as a video or images, and can relay animage, a video, or a video frame of a subject alongside or overlaid withclassification results. For example, the inspection system 900 canoverlay a graphical indicator atop an image and the graphical indicatorcan identify the detected threat or object. In some implementations, theimage overlaid with the graphical indicator can be provided withadditional metadata about the individual, detected object, or systemparameters. In some implementations, the image overlaid with thegraphical indicator can be provided to an individual, such as aninspection system operator or security guard who can be located furtherdownstream in the sequence of objects or individuals being inspected viathe inspection system. In this way, the inspection system can providethe image overlaid with the graphical indicator to the inspection systemoperator or security guard for additional monitoring and/or interceptionof the detected object, threat, or individual.

Although a few variations have been described in detail above, othermodifications or additions are possible. For example, the number ofreceivers is not limited and some implementations may include any numberof receivers. The transmitters are not limited to a particularfrequency, for example, coils with different properties (operatingfrequencies, locations, and the like) can be used. Differentreconstruction algorithms may be used and different features may be usedfor threat detection.

Without in any way limiting the scope, interpretation, or application ofthe claims appearing below, a technical effect of one or more of theexample implementations disclosed herein may include one or more of thefollowing, for example, some example implementations of the currentsubject matter can perform threat detection and discrimination in highclutter environments in which individuals may be carrying personal itemssuch as cell phones and laptops and without personal item divestment. Insome implementations, a personnel inspection system can perform threatdetection and discrimination with high throughput that allowsindividuals to pass through the metal detector at normal walking speedssuch that individuals are not required to slow down for inspection and,in some implementations, the inspection threshold can allow for multipleindividuals to pass through the threshold side-by-side (e.g., two ormore abreast). In some configurations, individuals walking in nearproximity can be screened, thereby eliminating the need for screenedindividuals to remain stationary during the screening process.

One or more aspects or features of the subject matter described hereincan be realized in digital electronic circuitry, integrated circuitry,specially designed application specific integrated circuits (ASICs),field programmable gate arrays (FPGAs) computer hardware, firmware,software, and/or combinations thereof. These various aspects or featurescan include implementation in one or more computer programs that areexecutable and/or interpretable on a programmable system including atleast one programmable processor, which can be special or generalpurpose, coupled to receive data and instructions from, and to transmitdata and instructions to, a storage system, at least one input device,and at least one output device. The programmable system or computingsystem may include clients and servers. A client and server aregenerally remote from each other and typically interact through acommunication network. The relationship of client and server arises byvirtue of computer programs running on the respective computers andhaving a client-server relationship to each other.

These computer programs, which can also be referred to as programs,software, software applications, applications, components, or code,include machine instructions for a programmable processor, and can beimplemented in a high-level procedural language, an object-orientedprogramming language, a functional programming language, a logicalprogramming language, and/or in assembly/machine language. As usedherein, the term “machine-readable medium” refers to any computerprogram product, apparatus and/or device, such as for example magneticdiscs, optical disks, memory, and Programmable Logic Devices (PLDs),used to provide machine instructions and/or data to a programmableprocessor, including a machine-readable medium that receives machineinstructions as a machine-readable signal. The term “machine-readablesignal” refers to any signal used to provide machine instructions and/ordata to a programmable processor. The machine-readable medium can storesuch machine instructions non-transitorily, such as for example as woulda non-transient solid-state memory or a magnetic hard drive or anyequivalent storage medium. The machine-readable medium can alternativelyor additionally store such machine instructions in a transient manner,such as for example as would a processor cache or other random accessmemory associated with one or more physical processor cores.

To provide for interaction with a user, one or more aspects or featuresof the subject matter described herein can be implemented on a computerhaving a display device, such as for example a cathode ray tube (CRT) ora liquid crystal display (LCD) or a light emitting diode (LED) monitorfor displaying information to the user and a keyboard and a pointingdevice, such as for example a mouse or a trackball, by which the usermay provide input to the computer. Other kinds of devices can be used toprovide for interaction with a user as well. For example, feedbackprovided to the user can be any form of sensory feedback, such as forexample visual feedback, auditory feedback, or tactile feedback; andinput from the user may be received in any form, including, but notlimited to, acoustic, speech, or tactile input. Other possible inputdevices include, but are not limited to, touch screens or othertouch-sensitive devices such as single or multi-point resistive orcapacitive trackpads, voice recognition hardware and software, opticalscanners, optical pointers, digital image capture devices and associatedinterpretation software, and the like.

In the descriptions above and in the claims, phrases such as “at leastone of” or “one or more of” may occur followed by a conjunctive list ofelements or features. The term “and/or” may also occur in a list of twoor more elements or features. Unless otherwise implicitly or explicitlycontradicted by the context in which it is used, such a phrase isintended to mean any of the listed elements or features individually orany of the recited elements or features in combination with any of theother recited elements or features. For example, the phrases “at leastone of A and B;” “one or more of A and B;” and “A and/or B” are eachintended to mean “A alone, B alone, or A and B together.” A similarinterpretation is also intended for lists including three or more items.For example, the phrases “at least one of A, B, and C;” “one or more ofA, B, and C;” and “A, B, and/or C” are each intended to mean “A alone, Balone, C alone, A and B together, A and C together, B and C together, orA and B and C together.” In addition, use of the term “based on,” aboveand in the claims is intended to mean, “based at least in part on,” suchthat an unrecited feature or element is also permissible.

The subject matter described herein can be embodied in systems,apparatus, methods, and/or articles depending on the desiredconfiguration. The implementations set forth in the foregoingdescription do not represent all implementations consistent with thesubject matter described herein. Instead, they are merely some examplesconsistent with aspects related to the described subject matter.Although a few variations have been described in detail above, othermodifications or additions are possible. In particular, further featuresand/or variations can be provided in addition to those set forth herein.For example, the implementations described above can be directed tovarious combinations and subcombinations of the disclosed featuresand/or combinations and subcombinations of several further featuresdisclosed above. In addition, the logic flows depicted in theaccompanying figures and/or described herein do not necessarily requirethe particular order shown, or sequential order, to achieve desirableresults. Other implementations may be within the scope of the followingclaims.

What is claimed is:
 1. A method comprising: receiving, from a pluralityof magnetic field receivers including magnetic sensors, datacharacterizing samples obtained by the plurality of magnetic fieldreceivers, the samples of a combination of a first magnetic field and asecond magnetic field resulting from interaction of the first magneticfield and an object; determining, using the received data, apolarizability index of the object, the polarizability indexcharacterizing a magnetic polarizability property of the object;classifying, using the determined polarizability index, the object asthreat or non-threat; and providing the classification.
 2. The method ofclaim 1, wherein the polarizability index of the object characterizes atleast a shape, a permeability, and a conductivity of the object.
 3. Themethod of claim 1, wherein the polarizability index of the objectincludes a complex tensor including at least six elements characterizingdirectional polarizability components of the object at one or morefrequencies employed by the transmitting system.
 4. The method of claim1, wherein determining the polarizability index includes solving a setof trial solutions via a precomputed pseudo-inverse, determining aresidual for each of the trial solutions, and selecting the trialsolution resulting in a smallest residual.
 5. The method of claim 1,wherein determining the polarizability index includes: defining a set oftrial solutions, each trial solution including a location, a speed, anda time-shift; calculating an associated polarizability index and anassociated residual for each trial solution; and selecting a final trialsolution, the final trial solution including the trial solution of theset of trial solutions that is associated with the smallest residual. 6.The method of claim 1, further comprising: localizing the object withina volume under inspection, the localization including determining anobject speed, an object position, and an object time-offset to apredetermined plane.
 7. The method of claim 1, further comprising:generating one or more signals for driving a magnetic field transmitterat a frequency that is less than 1,000 Hertz (Hz).
 8. The method ofclaim 1, wherein the plurality of magnetic field receivers include fluxgate sensors that measure a magnitude and phase of the combination ofthe first magnetic field and the second magnetic field in at least threeaxis at locations of the plurality of magnetic field receivers.
 9. Themethod of claim 1, wherein the samples obtained by the plurality ofmagnetic field receivers characterize a magnitude and a phase; andwherein classifying, using the polarizability index, the object asthreat or non-threat includes comparing the magnitude, the phase, andthe polarizability index to a library of predetermined threatsignatures.
 10. The method of claim 1, wherein the first magnetic fieldis generated by at least a first magnetic field transmitter and a secondmagnetic field transmitter, the first magnetic field transmitter and thesecond magnetic field transmitter oriented in a first direction, and thefirst magnetic field transmitter and the second magnetic fieldtransmitter spatially offset transverse to a predefined direction ofmotion and transverse to the first direction.
 11. A system comprising: amagnetic field transmitter configured to generate a first magneticfield; a plurality of magnetic field receivers including magneticsensors, the plurality of magnetic field receivers configured to samplea combination of the first magnetic field and a second magnetic fieldresulting from interaction of the first magnetic field and an object;and at least one data processor configured to at least: receive datacharacterizing the samples obtained by the plurality of magnetic fieldreceivers; determine, using the received data, a polarizability index ofthe object, the polarizability index characterizing a magneticpolarizability property of the object; classify, using thepolarizability index, the object as threat or non-threat; and providethe classification.
 12. The system of claim 11, further comprising: atransmit driver coupled to the magnetic field transmitter and configuredto generate one or more signals for driving the magnetic fieldtransmitter at a frequency that is less than 1,000 Hertz (Hz).
 13. Thesystem of claim 12, wherein the transmit driver is configured togenerate one or more signals for driving the magnetic field transmitterat a first frequency and a second frequency, the magnetic fieldtransmitter driven at the first frequency and the second frequency. 14.The system of claim 13, wherein the first frequency is between 5 Hz and100 Hz and the second frequency is between 100 Hz and 1,000 Hz.
 15. Thesystem of claim 11, further comprising a second magnetic fieldtransmitter, wherein the transmit driver further includes: first driveelectronics to drive the magnetic field transmitter; and second driveelectronics to drive the second magnetic field transmitter.
 16. Thesystem of claim 11, wherein the samples obtained by the plurality ofmagnetic field receivers characterize a magnitude and a phase; andwherein classifying, using the polarizability index, the object asthreat or non-threat includes comparing the magnitude, the phase, andthe polarizability index to a library of predetermined threatsignatures.
 17. The system of claim 11, wherein the plurality ofmagnetic field receivers include flux gate sensors that measure amagnitude and phase of the combination of the first magnetic field andthe second magnetic field in at least three axis at locations of theplurality of magnetic field receivers.
 18. The system of claim 11,further comprising: a data acquisition base station coupled to theplurality of magnetic field receivers and configured to filter,demodulate, and digitize the samples obtained by the plurality ofmagnetic field receivers; the data acquisition base station furtherconfigured to determine in-phase and quadrature data characterizing thesecond magnetic field.
 19. The system of claim 11, wherein determiningthe polarizability index includes determining a polarizability tensor.20. The system of claim 19, wherein determining the polarizability indexincludes solving a set of trial solutions via a precomputedpseudo-inverse, determining a residual for each of the trial solutions,and selecting the trial solution resulting in a smallest residual.